Performance evaluation of multiple classification of the ultrasonic supraspinatus images by using ML, RBFNN and SVM classifiers

Horng, Ming-Huwi

doi:10.1016/j.eswa.2009.11.008

Cited by 23 publications

(16 citation statements)

References 34 publications

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“…At present, the evaluation of supraspinatus tear using MR imaging involves either simple image enhancement performed manually or the input of data into a classifier for identification after feature extraction. 15,22 However, in this study, we combined 2 detection modes. In the first phase, MR images were enhanced to strengthen information regarding suspicious features, and the data were then entered into the classifier to perform the identification.…”

Section: Discussionmentioning

confidence: 99%

Combined Image Enhancement, Feature Extraction, and Classification Protocol to Improve Detection and Diagnosis of Rotator-cuff Tears on MR Imaging

Lin

Wang

et al. 2014

MRMS

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Purpose: The diagnosis of most rotator cuff tears (RCTs) relies upon magnetic resonance (MR) imaging, but direct capture of MR images without enhanced image processing leads to poor image contrast and potential misdiagnosis. Therefore, we developed a 2-stage model for the detection and diagnosis of injury of the supraspinatus tendon.Methods: The first stage used coupled weighted histogram separation (WHS) to improve image enhancement, and the second stage extracted suspicious texture, features of both spatial and spectral domains, and sequential floating forward selection (SFFS) selected features conducive to classification of RCTs. We then tested injuries of the supraspinatus tendon using the classifier.Results: The extraction of features by SFFS can increase detection of supraspinatus injury by reducing the input vector by 57.78% from the enhanced input images. The receiver operating characteristic (ROC) curve indicated an azimuth (Az) value of 84.38% when SFFS selected 76 features to construct a support vector machine (SVM) classifier from the enhanced images, compared with 56.94% when all 180 features from the raw input images were used for the construction.Conclusions: The performance of the classifier constructed by SFFS-selected features is superior to that using all features. These findings can serve as references to improve diagnosis and treatment of supraspinatus injuries.

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Section: Discussionmentioning

confidence: 99%

Combined Image Enhancement, Feature Extraction, and Classification Protocol to Improve Detection and Diagnosis of Rotator-cuff Tears on MR Imaging

Lin

Wang

et al. 2014

MRMS

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“…were selected the item with the best fitness function for every element, and then to be represented G best ,then the velocity element is calculated according to the equation (3),and updated location of the item, according to equation (4). Where they are conducting the following steps repeatedly until the maximum of the iterations within the algorithm or be achieved conditions stop-and-thus is obtain the best solution .…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Comparative Study between the (BA) Algorithm and (PSO) Algorithm to Train (RBF) Network at Data Classification

Talal¹

2014

IJCA

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The Swarm Intelligence Algorithms are (Meta-Heuristic) development Algorithms, which attracted much attention and appeared its ability in the last ten years within many applications such as data mining, scheduling, improve the performance of artificial neural networks (ANN) and classification. In this research was the work of a comparative study between Bat Algorithm (BA) and Particle Swarm Optimization Algorithm (PSO) to train Radial Basis function network (RBF) to classify types of benchmarking data. Results showed that Bat Algorithm (BA) is overcome on (PSO )Algorithm in terms of improving the weights of (RBF) network and accelerate the training time and good convergence of optimal solutions, which led to increase network efficiency and reduce falling mistakes and nonoccurrence.

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“…The actual topological structure of RBFNN is displayed in Figure 2. RBFNN is the kind of feed-forward artificial neural network with three layers (Figure 2): the input layer, the hidden layer and the output layer [7,8,12]. The input layer is composed of signal source nodes and the input vector can be mapped into hidden space directly in a nonlinear form.…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

“…Artificial neural networks are effective tools for stimulating such relationships [6]. A radial basis function neural network (RBFNN) is an artificial neural network with a simple structure and a fast learning speed, and it can approximate any continuous functions at any degree of accuracy [7,8]. On the other hand, soil chemical properties and the bio-available transformation of soil elements are the main indexes for representing soil fertility [9][10][11], and they have great impacts on crop yield and quality.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Helianthus annuus Seed Quality Based on Soil Chemical Properties Using Radial Basis Function Neural Networks

Wang

et al. 2016

Sustainability

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Forecasting crop chemical characteristics based on soil properties is not only a possible way to spare supplementary sampling and testing, but also a potential method of instructing cultivation planning based on regional soil surveys. In this paper, taking the data of regional agricultural geological survey on Helianthus annuus sources in the western part of the Jilin province as an attempt, radial basis function neural networks were used to forecast the quality indexes of Helianthus annuus seeds based on the non-linear relationship between soil and crop. The results indicate the following: (1) The mean relative errors of vitamin E, protein, fat, and TAA concentration forecasting neural networks are 2.63%, 2.19%, 2.19%, and 2.80%, respectively. The root mean square errors are 1.7 mg/100 g, 0.59%, 1.09%, and 0.77%. The forecasting radial basis function neural networks are of high prediction accuracy, which introduces an empirical case of forecasting the quality of crop based on a systematical soil environmental quality investigation along with a sampling survey of the crops. To set a proper model, interrelation between the selected indexes of input layer and output layer needs to be confirmed first, and a low setting of spread can improve the accuracy; (2) Soil in the studied area is under severe salinization, and concentrations of soil chemical properties mostly show an evident regional difference between the three experimental fields. However, the vitamin E, protein, and TAA concentrations of Helianthus annuus seeds all stabilize in a certain range despite the different soil environments. The mean fat concentration of Helianthus annuus seeds collected from Nongan and Daan exceeds those from Tongyu by approximately 5%, which shows a relatively evident regional difference.

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Performance evaluation of multiple classification of the ultrasonic supraspinatus images by using ML, RBFNN and SVM classifiers

Cited by 23 publications

References 34 publications

Combined Image Enhancement, Feature Extraction, and Classification Protocol to Improve Detection and Diagnosis of Rotator-cuff Tears on MR Imaging

Combined Image Enhancement, Feature Extraction, and Classification Protocol to Improve Detection and Diagnosis of Rotator-cuff Tears on MR Imaging

Comparative Study between the (BA) Algorithm and (PSO) Algorithm to Train (RBF) Network at Data Classification

Forecasting Helianthus annuus Seed Quality Based on Soil Chemical Properties Using Radial Basis Function Neural Networks

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